Causal Perspectives on Network Meta-Analysis
网络元分析的因果视角AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要的核心是目标总体、因果效应与聚合识别；其中网络指治疗比较网络，未涉及单位间互动或干扰，因此仅归入因果推断。
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网络元分析的因果视角
- 研究问题
- 如何先定义目标总体与治疗效应，再推导成对及网络元分析的聚合方式，使研究间效应修饰和中心差异得到明确的因果解释？
- 方法与思路
- 作者基于汇总数据建立统一因果框架，明确目标总体及异质性来源，再由识别推导治疗臂层面的聚合，而非预先指定效应对比聚合。
- 材料陈述的贡献
- 摘要称，该框架无需依赖治疗网络本身来识别效应；数值研究显示估计器针对明确因果目标，并指出其与传统方法可能产生不同结果。
- 阅读关注点
- 应核对目标总体的构造、效应修饰因素和中心效应的处理，以及治疗臂聚合成立的识别条件；重点理解不依赖治疗网络的具体含义。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列出完整识别条件，不能据此认定任意断连网络可识别或所有传统估计均无效。
原文摘要
Pairwise and network meta-analyses occupy the highest tier of evidence-based medicine and routinely inform clinical guidelines and healthcare decision-making. Current approaches typically aggregate study-level treatment effects to obtain an overall estimate. We argue that the causal estimand should come first, with the aggregation derived only afterwards: the target population and the relevant sources of between-study heterogeneity should be explicitly defined before deriving the aggregation required for identification. This shift in perspective fundamentally changes both the estimands and the methodology. We develop a unified causal framework for pairwise and network meta-analysis based on aggregate data. By defining treatment effects with respect to a clinically meaningful target population, for example, the average population represented by the contributing trials, and accounting for heterogeneity induced by treatment-effect modifiers and center effects, we show that identification naturally leads to arm-level aggregation. In the network setting, this causal formulation departs fundamentally from the conventional contrast-based paradigm: arm-level aggregation emerges from the causal formulation rather than from a modeling choice, and treatment effects are identified without relying on the treatment network itself. This perspective provides an additional conceptual argument in the long-standing contrast-based versus arm-based debate. Numerical studies show that the proposed estimators target well-defined causal effects, whereas the causal interpretation of conventional approaches remains unclear. Although both approaches often produce similar estimates, we identify settings in which they diverge, with potentially important implications for the interpretation of meta-analytic evidence.